A MIMO (multiple input multiple output) system is a communication system with multiple antennas at both the transmit side and the receive side. If nT denotes the number of transmit antennas and nR denotes the number of receive antennas, a MIMO system achieves a channel capacity which is almost N=min(nT, nR) times of the capacity of a corresponding single antenna system.
To achieve this capacity of a MIMO system, a spatial multiplexing scheme has been proposed according to which one independent data stream is transmitted by each transmit antenna. Because of the cross-interference among the data streams, each receive antenna receives a combination of the transmitted data streams. It is a challenging task for the receiver to separate the transmitted data streams, especially when the signal dimension N is very large.
A single-antenna communication system usually suffers from inter-symbol interference (ISI) and multiple access interference (MAI). For example, in single carrier cyclic prefix (SCCP) based systems or WHT-OFDM (Walsh Hadamard transform orthogonal frequency division multiplexing) systems, the transmitted symbols interfere with each other, yielding severe ISI. Both MAI and ISI exist in code division multiple access (CDMA) systems.
In wired transmission systems such as in digital subscriber lines (DSL), multiple lines are bonded together, thus besides the severe ISI due to the long delay in each telephone line, cross-talk among different lines is another major impairment affecting the transmission quality.
For a MIMO system, the following input-output model can be used:x=Hs+n  (1)where s denotes the transmitted signal vector, x denotes the received signal vector and n denotes the received noise vector. H denotes the channel matrix and represents the channel responses from the transmit side to the receive side. For clarity, it is assumed that s, x and n are all N×1 and H is a N×N matrix.
The MIMO model given above represents a wide range of communication systems. In fact, the input-output relation can be derived either in time domain, frequency domain, spatial domain or any combination of them. For a MIMO antenna system working in flat fading environment, the channel matrix H represents the channel responses from each transmit antenna to each receive antenna. For a SCCP system, the channel matrix H is a circular matrix, the elements of which come from the time domain channel responses from the transmit antenna to the receive antenna.
In the above model, the transmitted signal vector s may consist of only user's information-bearing symbols, which are usually with the same modulation. For multiuser case, the transmitted symbols may use different modulation methods.
For a generic MIMO system, three types of interferences must be considered: multi-stream interference (MSI), inter-symbol interference (MSI), both from one user and MAI which comes from the other users.
Linear equalizers and nonlinear equalizers have been proposed to recover transmitted signals. Linear detectors, such as ZF (zero forcing) detectors and MMSE (minimum mean squared error) detectors and some nonlinear detectors, such as generalized decision feedback equalizers (GDFE) are simple for implementation, but achieve a detector performance far away from the maximum likelihood (ML) performance bound. When the signal dimension is very small, a nonlinear exhaustive search can be applied to achieve ML detection, where the number of candidates to be sought is MN, where M is the constellation size of the modulated symbols. For large signal dimension, however, the exponential complexity of the ML detector makes it impractical for implementation.
Recently, much effort has been devoted in designing low-complexity non-linear receivers to achieve near-ML performance for MIMO systems. When the signal dimension is small and moderate, near ML detection can be achieved through closest lattice point search (CPS) techniques, such as sphere decoding, which have lower complexity than the brute force ML detector. The time and space complexity for CPS techniques grows dramatically with the increase of the signal dimension. Therefore, it becomes impractical to use CPS when the signal dimension goes to large, say over 100.
A block-iterative decision feedback equalizer (BI-DFE) method has been proposed for ISI/MAI cancellation in CDMA systems in [1] and for ISI channel equalization in single user systems in [2], assuming an infinite signal dimension. This method iteratively estimates the overall transmitted symbols and uses these decision-directed symbols to obtain a new set of symbol estimates by canceling out the interferences. As an iterative technique, for high SNR (signal to noise ratio) region and when the number of independent delay paths is sufficiently large, BI-DFE can achieve the matched filter bound (MFB) within three to five iterations.
BI-DFE was later extended to frequency domain equalization for SCCP systems in [3], and to layered space-time processing for multiple antenna SCCP systems [4], both with finite signal dimensions. For SCCP systems, the channel is a circular matrix. For multiple antenna SCCP systems, the interferences from the other users are cancelled out, thus the newly generated signal is actually a single user SCCP system, to which a BI-DFE is applied. Therefore, both extensions make use of the channel's known structure—circular matrix, which is not available in generic MIMO channels.
An object of the invention is to provide an improved method for equalizing a digital signal compared to prior art methods.
The object is achieved by a method for equalizing a digital signal and an equalizer with the features according to the independent claims.